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Merge pull request #37 from Runware/feature-upscaleAndBackgroundRemovalProviderSettings/OviSupport

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Add provider settings support and new input nodes for upscale/background removal
This commit is contained in:
Sirshendu Ganguly
2025-10-28 23:11:35 -04:00
committed by GitHub
parent 52ff9c5cff 843422e01c
commit 7f80c78761
25 changed files with 468 additions and 65 deletions
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__init__.py
+4
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@@ -50,6 +50,8 @@ from .modules.vectorize import vectorize
from .modules.videoBgRemoval import videoBgRemoval
from .modules.videoUpscaler import videoUpscaler
from .modules.videoInputsReferences import videoInputsReferences
from .modules.safetyInputs import safetyInputs
from .modules.videoAdvancedFeatureInputs import videoAdvancedFeatureInputs
RUNWARE_COMFYUI_VERSION = "1.4.0 Beta"
@@ -114,6 +116,8 @@ NODE_CLASS_MAPPINGS = {
"Runware Video Background Removal": videoBgRemoval,
"Runware Video Upscaler": videoUpscaler,
"Runware Video Inputs References": videoInputsReferences,
"Runware Safety Inputs": safetyInputs,
"Runware Video Advanced Feature Inputs": videoAdvancedFeatureInputs,
}
NODE_DISPLAY_NAME_MAPPINGS = {
clientlibs/types.js
+30
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@@ -85,6 +85,7 @@ const RUNWARE_NODE_TYPES = {
PIXVERSEPROVIDERSETTINGS: "Runware Pixverse Provider Settings",
OPENAIPROVIDERSETTINGS: "Runware OpenAI Provider Settings",
BYTEDANCEPROVIDERSETTINGS: "Runware Bytedance Provider Settings",
BRIAPROVIDERSETTINGS: "Runware Bria Provider Settings",
ELEVENLABSPROVIDERSETTINGS: "Runware ElevenLabs Provider Settings",
ELEVENLABSPROVIDERSETTINGSSECTIONS: "Runware ElevenLabs Provider Settings Sections",
VIDUPROVIDERSETTINGS: "Runware Vidu Provider Settings",
@@ -97,6 +98,11 @@ const RUNWARE_NODE_TYPES = {
VIDEOBGREMOVAL: "Runware Video Background Removal",
VIDEOUPSCALER: "Runware Video Upscaler",
VIDEOINPUTSREFERENCES: "Runware Video Inputs References",
ACCELERATOROPTIONS: "Runware Accelerator Options",
VIDEOTRANSCRIPTION: "Runware Video Transcription",
MINIMAXPROVIDERSETTINGS: "Runware MiniMax Provider Settings",
SAFETYINPUTS: "Runware Safety Inputs",
VIDEOADVANCEDFEATUREINPUTS: "Runware Video Advanced Feature Inputs",
};
const RUNWARE_NODE_PROPS = {
@@ -251,6 +257,10 @@ const RUNWARE_NODE_PROPS = {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.BRIAPROVIDERSETTINGS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.ELEVENLABSPROVIDERSETTINGS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
@@ -296,6 +306,26 @@ const RUNWARE_NODE_PROPS = {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.ACCELERATOROPTIONS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.VIDEOTRANSCRIPTION]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.MINIMAXPROVIDERSETTINGS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.SAFETYINPUTS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.VIDEOADVANCEDFEATUREINPUTS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
};
export {
modules/acceleratorOptions.py
+78 -42
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@@ -11,9 +11,13 @@ class acceleratorOptions:
"tooltip": "Enable Frame Buffer Cache for faster generation",
"default": None,
}),
"useCacheDistance": ("BOOLEAN", {
"tooltip": "Enable to include cacheDistance parameter in API request.",
"default": False,
}),
"cacheDistance": ("FLOAT", {
"tooltip": "Cache distance parameter for FBCache",
"default": None,
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
@@ -24,9 +28,13 @@ class acceleratorOptions:
"tooltip": "Enable TeaCache for transformer-based models (Flux, SD3). Does not work with UNet models like SDXL or SD1.5. Particularly effective for iterative editing workflows.",
"default": None,
}),
"useTeaCacheDistance": ("BOOLEAN", {
"tooltip": "Enable to include teaCacheDistance parameter in API request.",
"default": False,
}),
"teaCacheDistance": ("FLOAT", {
"tooltip": "Controls TeaCache aggressiveness: 0.0 (conservative/quality) to 1.0 (aggressive/speed). Lower values maintain quality, higher values prioritize speed.",
"default": None,
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
@@ -37,47 +45,65 @@ class acceleratorOptions:
"tooltip": "Enable DeepCache for UNet-based models (SDXL, SD1.5). Not applicable to transformer models. Provides performance improvements for high-throughput scenarios.",
"default": None,
}),
"useDeepCacheOptions": ("BOOLEAN", {
"tooltip": "Enable to include DeepCache options (deepCacheInterval and deepCacheBranchId) in API request.",
"default": False,
}),
"deepCacheInterval": ("INT", {
"tooltip": "Frequency of feature caching - steps between cache operations. Larger values = faster but may impact quality. Smaller values = better quality but slower.",
"default": None,
"default": 1,
"min": 1,
"max": 20,
}),
"deepCacheBranchId": ("INT", {
"tooltip": "Network branch for caching (0=shallowest, deeper=more conservative). Lower IDs = aggressive caching/faster, higher IDs = conservative caching/better quality.",
"default": None,
"default": 0,
"min": 0,
"max": 10,
}),
# Cache step control parameters
# Cache step control parameters (step numbers)
"useCacheSteps": ("BOOLEAN", {
"tooltip": "Enable to include cache step control parameters (step numbers) in API request.",
"default": False,
}),
"cacheStartStep": ("INT", {
"tooltip": "Inference step number where caching begins (0 to total steps). Alternative to cacheStartStepPercentage.",
"default": None,
"tooltip": "Inference step number where caching begins (0 to total steps). Only included when useCacheSteps is enabled and value > 0.",
"default": 0,
"min": 0,
"max": 200,
}),
"cacheEndStep": ("INT", {
"tooltip": "Inference step number where caching stops (must be > cacheStartStep). Only included when useCacheSteps is enabled.",
"default": 1,
"min": 1,
"max": 200,
}),
# Cache step control parameters (percentages)
"useCachePercentageSteps": ("BOOLEAN", {
"tooltip": "Enable to include cache step control parameters (percentages) in API request.",
"default": False,
}),
"cacheStartStepPercentage": ("INT", {
"tooltip": "Percentage of total steps where caching begins (0-99). Alternative to cacheStartStep.",
"default": None,
"tooltip": "Percentage of total steps where caching begins (0-99). Only included when useCachePercentageSteps is enabled and value > 0.",
"default": 0,
"min": 0,
"max": 99,
}),
"cacheEndStep": ("INT", {
"tooltip": "Inference step number where caching stops (must be > cacheStartStep). Alternative to cacheEndStepPercentage.",
"default": None,
"min": 1,
"max": 200,
}),
"cacheEndStepPercentage": ("INT", {
"tooltip": "Percentage of total steps where caching stops (must be > cacheStartStepPercentage). Alternative to cacheEndStep.",
"default": None,
"tooltip": "Percentage of total steps where caching stops (must be > cacheStartStepPercentage). Only included when useCachePercentageSteps is enabled.",
"default": 1,
"min": 1,
"max": 100,
}),
"useCacheMaxConsecutiveSteps": ("BOOLEAN", {
"tooltip": "Enable to include cacheMaxConsecutiveSteps parameter in API request.",
"default": False,
}),
"cacheMaxConsecutiveSteps": ("INT", {
"tooltip": "Max consecutive steps using cached computations before forcing fresh computation. Prevents quality degradation from extended cache reuse.",
"default": None,
"default": 1,
"min": 1,
"max": 5,
}),
@@ -93,51 +119,61 @@ class acceleratorOptions:
def acceleratorOptions(self, **kwargs):
# Get all optional parameters
fbcache = kwargs.get("fbcache")
cacheDistance = kwargs.get("cacheDistance")
useCacheDistance = kwargs.get("useCacheDistance", False)
cacheDistance = kwargs.get("cacheDistance", 0.0)
teaCache = kwargs.get("teaCache")
teaCacheDistance = kwargs.get("teaCacheDistance")
useTeaCacheDistance = kwargs.get("useTeaCacheDistance", False)
teaCacheDistance = kwargs.get("teaCacheDistance", 0.0)
deepCache = kwargs.get("deepCache")
deepCacheInterval = kwargs.get("deepCacheInterval")
deepCacheBranchId = kwargs.get("deepCacheBranchId")
cacheStartStep = kwargs.get("cacheStartStep")
cacheStartStepPercentage = kwargs.get("cacheStartStepPercentage")
cacheEndStep = kwargs.get("cacheEndStep")
cacheEndStepPercentage = kwargs.get("cacheEndStepPercentage")
cacheMaxConsecutiveSteps = kwargs.get("cacheMaxConsecutiveSteps")
# Get toggle and value parameters
useDeepCacheOptions = kwargs.get("useDeepCacheOptions", False)
deepCacheInterval = kwargs.get("deepCacheInterval", 1)
deepCacheBranchId = kwargs.get("deepCacheBranchId", 0)
useCacheSteps = kwargs.get("useCacheSteps", False)
cacheStartStep = kwargs.get("cacheStartStep", 0)
cacheEndStep = kwargs.get("cacheEndStep", 1)
useCachePercentageSteps = kwargs.get("useCachePercentageSteps", False)
cacheStartStepPercentage = kwargs.get("cacheStartStepPercentage", 0)
cacheEndStepPercentage = kwargs.get("cacheEndStepPercentage", 1)
useCacheMaxConsecutiveSteps = kwargs.get("useCacheMaxConsecutiveSteps", False)
cacheMaxConsecutiveSteps = kwargs.get("cacheMaxConsecutiveSteps", 1)
# Build accelerator options dictionary
acceleratorOptions = {}
# FBCache options
if fbcache is not None:
if fbcache is True:
acceleratorOptions["fbcache"] = fbcache
if cacheDistance is not None:
if useCacheDistance:
acceleratorOptions["cacheDistance"] = cacheDistance
# TeaCache options
if teaCache is not None:
if teaCache is True:
acceleratorOptions["teaCache"] = teaCache
if teaCacheDistance is not None:
if useTeaCacheDistance:
acceleratorOptions["teaCacheDistance"] = teaCacheDistance
# DeepCache options
if deepCache is not None:
if deepCache is True:
acceleratorOptions["deepCache"] = deepCache
if deepCacheInterval is not None:
if useDeepCacheOptions:
acceleratorOptions["deepCacheInterval"] = deepCacheInterval
if deepCacheBranchId is not None:
acceleratorOptions["deepCacheBranchId"] = deepCacheBranchId
# Cache step control options
if cacheStartStep is not None:
acceleratorOptions["cacheStartStep"] = cacheStartStep
if cacheStartStepPercentage is not None:
acceleratorOptions["cacheStartStepPercentage"] = cacheStartStepPercentage
if cacheEndStep is not None:
# Cache step control options (step numbers)
if useCacheSteps:
if cacheStartStep > 0:
acceleratorOptions["cacheStartStep"] = cacheStartStep
acceleratorOptions["cacheEndStep"] = cacheEndStep
if cacheEndStepPercentage is not None:
# Cache step control options (percentages)
if useCachePercentageSteps:
if cacheStartStepPercentage > 0:
acceleratorOptions["cacheStartStepPercentage"] = cacheStartStepPercentage
acceleratorOptions["cacheEndStepPercentage"] = cacheEndStepPercentage
if cacheMaxConsecutiveSteps is not None:
if useCacheMaxConsecutiveSteps:
acceleratorOptions["cacheMaxConsecutiveSteps"] = cacheMaxConsecutiveSteps
return (acceleratorOptions,)
modules/bgremoval.py
+53 -1
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@@ -1,9 +1,11 @@
from .utils import runwareUtils as rwUtils
import json
class bgremoval:
RUNWARE_RMBG_MODELS = {
"RemBG 1.4": "runware:109@1",
"Bria RMBG 2.0": "runware:110@1",
"Bria RMBG v2.0": "bria:2@1",
"BiRefNet v1 Base": "runware:112@1",
"BiRefNet v1 Base - COD": "runware:112@2",
"BiRefNet Dis": "runware:112@3",
@@ -64,6 +66,15 @@ class bgremoval:
"min": 1,
"max": 255,
}),
"safetyInputs": ("RUNWARESAFETYINPUTS", {
"tooltip": "Connect Runware Safety Inputs node to configure safety and content moderation settings.",
}),
"Accelerator": ("RUNWAREACCELERATOR", {
"tooltip": "Connect a Runware Accelerator Options Node to configure caching and acceleration settings.",
}),
"providerSettings": ("RUNWAREPROVIDERSETTINGS", {
"tooltip": "Connect a Runware Provider Settings node to configure provider-specific parameters.",
}),
**rwUtils.RUNWARE_REMBG_OUTPUT_FORMATS,
}
}
@@ -83,6 +94,9 @@ class bgremoval:
alphaMattingBackgroundThreshold = kwargs.get("Alpha Matting Background Threshold", 10)
alphaMattingErodeSize = kwargs.get("Alpha Matting Erode Size", 10)
outputFormat = kwargs.get("outputFormat", "WEBP")
safetyInputs = kwargs.get("safetyInputs", None)
runwareAccelerator = kwargs.get("Accelerator", None)
providerSettings = kwargs.get("providerSettings", None)
modelAIR = self.RUNWARE_RMBG_MODELS.get(modelName, "runware:109@1")
includeExtraSettings = postProcessMask or returnOnlyMask or alphaMatting
@@ -113,7 +127,45 @@ class bgremoval:
settings["alphaMattingBackgroundThreshold"] = alphaMattingBackgroundThreshold
settings["alphaMattingErodeSize"] = alphaMattingErodeSize
genConfig["settings"] = settings
# Add safety inputs if provided
if safetyInputs is not None and isinstance(safetyInputs, dict) and len(safetyInputs) > 0:
genConfig["safety"] = safetyInputs
# Add accelerator options if provided
if runwareAccelerator is not None and isinstance(runwareAccelerator, dict) and len(runwareAccelerator) > 0:
genConfig["acceleratorOptions"] = runwareAccelerator
# Handle providerSettings - extract provider name from model and merge with custom settings
if providerSettings is not None:
# Extract provider name from model (e.g., "bria:2@1" -> "bria", "runware:109@1" -> "runware")
provider_name = modelAIR.split(":")[0] if ":" in modelAIR else "runware"
# If providerSettings is a dictionary, create the correct API format
if isinstance(providerSettings, dict):
# Create the providerSettings object with provider name as key
final_provider_settings = {
provider_name: providerSettings
}
genConfig["providerSettings"] = final_provider_settings
print(f"[Debugging] Provider settings: {final_provider_settings}")
else:
# If it's just a string, use it directly
genConfig["providerSettings"] = providerSettings
genResult = rwUtils.inferenecRequest([genConfig])
# Debug: Print the request being sent
print(f"[DEBUG] Sending Background Removal Request:", flush=True)
print(f"[DEBUG] Request Payload: {json.dumps([genConfig], indent=2)}", flush=True)
try:
genResult = rwUtils.inferenecRequest([genConfig])
# Debug: Print the response received
print(f"[DEBUG] Received Background Removal Response:", flush=True)
print(f"[DEBUG] Response: {json.dumps(genResult, indent=2)}", flush=True)
except Exception as e:
print(f"[DEBUG] Error in Background Removal Request: {str(e)}", flush=True)
raise
images = rwUtils.convertImageB64List(genResult)
return (images, )
modules/briaImageProviderSettings.py
+13 -2
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@@ -14,7 +14,7 @@ class RunwareBriaProviderSettings:
return {
"required": {},
"optional": {
"medium": (["photography", "art"], {
"medium": (["None", "photography", "art"], {
"tooltip": "Select the medium type for image generation",
"default": "photography",
}),
@@ -48,6 +48,12 @@ class RunwareBriaProviderSettings:
"label_on": "Enabled",
"label_off": "Disabled",
}),
"preserveAlpha": ("BOOLEAN", {
"tooltip": "Preserve alpha channel in the image.",
"default": None,
"label_on": "Enabled",
"label_off": "Disabled",
}),
}
}
@@ -67,12 +73,13 @@ class RunwareBriaProviderSettings:
promptContentModeration = kwargs.get("promptContentModeration", True)
contentModeration = kwargs.get("contentModeration", True)
ipSignal = kwargs.get("ipSignal", False)
preserveAlpha = kwargs.get("preserveAlpha", None)
# Build settings dictionary
settings = {}
# Add medium
if medium is not None:
if medium is not None and medium != "None":
settings["medium"] = medium
# Add promptEnhancement
@@ -95,6 +102,10 @@ class RunwareBriaProviderSettings:
if ipSignal is not None:
settings["ipSignal"] = ipSignal
# Add preserveAlpha
if preserveAlpha is not None:
settings["preserveAlpha"] = preserveAlpha
# Clean up None values
settings = {k: v for k, v in settings.items() if v is not None}
modules/safetyInputs.py
+73
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@@ -0,0 +1,73 @@
from typing import Optional, Dict, Any
class safetyInputs:
"""Runware Safety Inputs Node"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {},
"optional": {
"mode": (["none", "fast", "full"], {
"tooltip": "Safety moderation mode: none (disable), fast (quick check), or full (comprehensive check).",
"default": "none",
}),
"tolerance": ("BOOLEAN", {
"tooltip": "Safety tolerance setting for content moderation.",
"default": None,
"label_on": "Enabled",
"label_off": "Disabled",
}),
"checkInputs": ("BOOLEAN", {
"tooltip": "Enable checking of input content for safety violations.",
"default": None,
"label_on": "Enabled",
"label_off": "Disabled",
}),
"checkContent": ("BOOLEAN", {
"tooltip": "Enable checking of generated content for safety violations.",
"default": None,
"label_on": "Enabled",
"label_off": "Disabled",
}),
}
}
RETURN_TYPES = ("RUNWARESAFETYINPUTS",)
RETURN_NAMES = ("Safety Inputs",)
FUNCTION = "create_safety_inputs"
CATEGORY = "Runware"
DESCRIPTION = "Configure safety and content moderation settings for Runware tasks."
def create_safety_inputs(self, **kwargs) -> tuple[Dict[str, Any]]:
"""Create safety inputs dictionary"""
# Get parameters
mode = kwargs.get("mode", None)
tolerance = kwargs.get("tolerance", None)
checkInputs = kwargs.get("checkInputs", None)
checkContent = kwargs.get("checkContent", None)
# Build safety inputs dictionary - only include non-None values
safety_inputs = {}
if mode is not None:
safety_inputs["mode"] = mode
if tolerance is not None:
safety_inputs["tolerance"] = tolerance
if checkInputs is not None:
safety_inputs["checkInputs"] = checkInputs
if checkContent is not None:
safety_inputs["checkContent"] = checkContent
return (safety_inputs,)
# Node class mappings
NODE_CLASS_MAPPINGS = {
"RunwareSafetyInputs": safetyInputs,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RunwareSafetyInputs": "Runware Safety Inputs",
}
modules/upscaler.py
+53 -2
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@@ -1,4 +1,5 @@
from .utils import runwareUtils as rwUtils
import json
class upscaler:
@classmethod
@@ -18,7 +19,7 @@ class upscaler:
},
"optional": {
"model": ("STRING", {
"tooltip": "Model AIR ID (runware:500@1, runware:501@1, runware:502@1, runware:503@1)",
"tooltip": "Model AIR ID (runware:500@1, runware:501@1, runware:502@1, runware:503@1, bria:52@1)",
"default": "",
}),
# Common parameters toggles
@@ -113,6 +114,15 @@ class upscaler:
"min": 0,
"max": 2,
}),
"safetyInputs": ("RUNWARESAFETYINPUTS", {
"tooltip": "Connect Runware Safety Inputs node to configure safety and content moderation settings.",
}),
"Accelerator": ("RUNWAREACCELERATOR", {
"tooltip": "Connect a Runware Accelerator Options Node to configure caching and acceleration settings.",
}),
"providerSettings": ("RUNWAREPROVIDERSETTINGS", {
"tooltip": "Connect a Runware Provider Settings node to configure provider-specific parameters.",
}),
},
}
@@ -147,6 +157,9 @@ class upscaler:
colorFix = kwargs.get("colorFix", False)
tileDiffusion = kwargs.get("tileDiffusion", False)
clipSkip = kwargs.get("clipSkip", 0)
safetyInputs = kwargs.get("safetyInputs", None)
runwareAccelerator = kwargs.get("Accelerator", None)
providerSettings = kwargs.get("providerSettings", None)
# Build the base configuration
genConfig = {
@@ -197,7 +210,45 @@ class upscaler:
# Add settings to config if any settings were specified
if settings:
genConfig["settings"] = settings
# Add safety inputs if provided
if safetyInputs is not None and isinstance(safetyInputs, dict) and len(safetyInputs) > 0:
genConfig["safety"] = safetyInputs
# Add accelerator options if provided
if runwareAccelerator is not None and isinstance(runwareAccelerator, dict) and len(runwareAccelerator) > 0:
genConfig["acceleratorOptions"] = runwareAccelerator
# Handle providerSettings - extract provider name from model and merge with custom settings
if providerSettings is not None:
# Extract provider name from model (e.g., "runware:500@1" -> "runware")
provider_name = model.split(":")[0] if ":" in model and model else "runware"
# If providerSettings is a dictionary, create the correct API format
if isinstance(providerSettings, dict):
# Create the providerSettings object with provider name as key
final_provider_settings = {
provider_name: providerSettings
}
genConfig["providerSettings"] = final_provider_settings
print(f"[Debugging] Provider settings: {final_provider_settings}")
else:
# If it's just a string, use it directly
genConfig["providerSettings"] = providerSettings
genResult = rwUtils.inferenecRequest([genConfig])
# Debug: Print the request being sent
print(f"[DEBUG] Sending Image Upscale Request:", flush=True)
print(f"[DEBUG] Request Payload: {json.dumps([genConfig], indent=2)}", flush=True)
try:
genResult = rwUtils.inferenecRequest([genConfig])
# Debug: Print the response received
print(f"[DEBUG] Received Image Upscale Response:", flush=True)
print(f"[DEBUG] Response: {json.dumps(genResult, indent=2)}", flush=True)
except Exception as e:
print(f"[DEBUG] Error in Image Upscale Request: {str(e)}", flush=True)
raise
images = rwUtils.convertImageB64List(genResult)
return (images, )
modules/videoAdvancedFeatureInputs.py
+84
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@@ -0,0 +1,84 @@
from typing import Optional, Dict, Any
class videoAdvancedFeatureInputs:
"""Runware Video Advanced Feature Inputs Node"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {},
"optional": {
"videoCFGScale": ("FLOAT", {
"tooltip": "CFG scale for video generation.",
"default": None,
"min": 0.0,
"max": 30.0,
"step": 0.1,
}),
"audioCFGScale": ("FLOAT", {
"tooltip": "CFG scale for audio generation.",
"default": None,
"min": 0.0,
"max": 30.0,
"step": 0.1,
}),
"videoNegativePrompt": ("STRING", {
"tooltip": "Negative prompt for video generation.",
"multiline": True,
"default": "",
}),
"audioNegativePrompt": ("STRING", {
"tooltip": "Negative prompt for audio generation.",
"multiline": True,
"default": "",
}),
"slgLayer": ("INT", {
"tooltip": "SLG layer setting.",
"default": None,
"min": 0,
"max": 100,
}),
}
}
RETURN_TYPES = ("RUNWAREVIDEOADVANCEDFEATUREINPUTS",)
RETURN_NAMES = ("Video Advanced Feature Inputs",)
FUNCTION = "create_advanced_feature_inputs"
CATEGORY = "Runware"
DESCRIPTION = "Configure advanced video feature inputs including CFG scales, negative prompts, and SLG layer settings."
def create_advanced_feature_inputs(self, **kwargs) -> tuple[Dict[str, Any]]:
"""Create video advanced feature inputs dictionary"""
# Get parameters
videoCFGScale = kwargs.get("videoCFGScale", None)
audioCFGScale = kwargs.get("audioCFGScale", None)
videoNegativePrompt = kwargs.get("videoNegativePrompt", None)
audioNegativePrompt = kwargs.get("audioNegativePrompt", None)
slgLayer = kwargs.get("slgLayer", None)
# Build advanced feature inputs dictionary - only include non-None values
advanced_inputs = {}
if videoCFGScale is not None:
advanced_inputs["videoCFGScale"] = videoCFGScale
if audioCFGScale is not None:
advanced_inputs["audioCFGScale"] = audioCFGScale
if videoNegativePrompt is not None and videoNegativePrompt.strip() != "":
advanced_inputs["videoNegativePrompt"] = videoNegativePrompt.strip()
if audioNegativePrompt is not None and audioNegativePrompt.strip() != "":
advanced_inputs["audioNegativePrompt"] = audioNegativePrompt.strip()
if slgLayer is not None:
advanced_inputs["slgLayer"] = slgLayer
return (advanced_inputs,)
# Node class mappings
NODE_CLASS_MAPPINGS = {
"RunwareVideoAdvancedFeatureInputs": videoAdvancedFeatureInputs,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RunwareVideoAdvancedFeatureInputs": "Runware Video Advanced Feature Inputs",
}
modules/videoInference.py
+50 -4
View File
@@ -42,6 +42,10 @@ class txt2vid:
"max": 1920,
"step": 1,
}),
"useDuration": ("BOOLEAN", {
"tooltip": "Enable to include duration parameter in API request. Disable if your model doesn't support duration.",
"default": True,
}),
"duration": ("INT", {
"tooltip": "The duration of the video in seconds.",
"default": 5,
@@ -72,6 +76,16 @@ class txt2vid:
"min": 1,
"max": 9223372036854776000,
}),
"useSteps": ("BOOLEAN", {
"tooltip": "Enable to include steps parameter in API request. Disable if your model doesn't support steps.",
"default": False,
}),
"steps": ("INT", {
"tooltip": "Number of inference steps for video generation. More steps generally result in higher quality but longer generation time.",
"default": 20,
"min": 1,
"max": 100,
}),
"batchSize": ("INT", {
"tooltip": "The number of videos to generate in a single request.",
"default": 1,
@@ -115,6 +129,15 @@ class txt2vid:
"inputs": ("RUNWAREVIDEOINFERENCEINPUTS", {
"tooltip": "Connect a Runware Video Inference Inputs node to provide custom inputs like image, audio, mask, and parameters for OmniHuman 1.5 and other video models.",
}),
"safetyInputs": ("RUNWARESAFETYINPUTS", {
"tooltip": "Connect Runware Safety Inputs node to configure safety and content moderation settings.",
}),
"videoAdvancedFeatureInputs": ("RUNWAREVIDEOADVANCEDFEATUREINPUTS", {
"tooltip": "Connect Runware Video Advanced Feature Inputs node to configure advanced video features like CFG scales, FPS, negative prompts, and SLG layer settings.",
}),
"Accelerator": ("RUNWAREACCELERATOR", {
"tooltip": "Connect a Runware Accelerator Options Node to configure caching and acceleration settings.",
}),
}
}
@@ -149,9 +172,13 @@ class txt2vid:
speechVoice = kwargs.get("speechVoice", None)
speechText = kwargs.get("speechText", None)
inputs = kwargs.get("inputs", None)
safetyInputs = kwargs.get("safetyInputs", None)
videoAdvancedFeatureInputs = kwargs.get("videoAdvancedFeatureInputs", None)
runwareAccelerator = kwargs.get("Accelerator", None)
useCustomDimensions = kwargs.get("useCustomDimensions", False)
customWidth = kwargs.get("width", 864)
customHeight = kwargs.get("height", 480)
useDuration = kwargs.get("useDuration", True)
duration = kwargs.get("duration", 5)
fps = kwargs.get("fps", 24)
useFps = kwargs.get("useFps", True)
@@ -159,6 +186,8 @@ class txt2vid:
batchSize = kwargs.get("batchSize", 1)
seed = kwargs.get("seed", 1)
useSeed = kwargs.get("useSeed", True)
steps = kwargs.get("steps", 20)
useSteps = kwargs.get("useSteps", False)
# Handle model input - could be dict or string
if isinstance(runwareVideoModel, dict):
@@ -181,14 +210,15 @@ class txt2vid:
{
"taskType": "videoInference",
"taskUUID": rwUtils.genRandUUID(),
"height": height,
"width": width,
"model": model,
"outputFormat": outputFormat,
"numberResults": batchSize,
"includeCost": True,
}
]
if height!=0 and width!=0:
genConfig[0]['height'] = height
genConfig[0]['width'] = width
# Only add positivePrompt if it's not empty
if positivePrompt and positivePrompt.strip() != "":
@@ -199,12 +229,16 @@ class txt2vid:
genConfig[0]["fps"] = fps
# Add duration parameter - unified for all models
genConfig[0]["duration"] = duration
if useDuration:
genConfig[0]["duration"] = duration
# Add seed parameter only if enabled
if useSeed:
genConfig[0]["seed"] = seed
# Add steps parameter only if enabled
if useSteps:
genConfig[0]["steps"] = steps
if (negativePrompt is not None and negativePrompt != ""):
genConfig[0]["negativePrompt"] = negativePrompt
@@ -273,6 +307,18 @@ class txt2vid:
else:
# If it's just a string, use it directly
genConfig[0]["providerSettings"] = providerSettings
# Add safety inputs if provided
if safetyInputs is not None and isinstance(safetyInputs, dict) and len(safetyInputs) > 0:
genConfig[0]["safetyInputs"] = safetyInputs
# Add video advanced feature inputs if provided
if videoAdvancedFeatureInputs is not None and isinstance(videoAdvancedFeatureInputs, dict) and len(videoAdvancedFeatureInputs) > 0:
genConfig[0]["advancedFeatures"] = videoAdvancedFeatureInputs
# Add accelerator options if provided
if runwareAccelerator is not None and isinstance(runwareAccelerator, dict) and len(runwareAccelerator) > 0:
genConfig[0]["acceleratorOptions"] = runwareAccelerator
if (multiInferenceMode):
return (None, genConfig)
modules/videoInferenceInputs.py
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@@ -15,6 +15,9 @@ class videoInferenceInputs:
"Mask": ("IMAGE", {
"tooltip": "Mask image to specify a specific subject in the image to speak. Use white/black mask format."
}),
"Frame": ("IMAGE", {
"tooltip": "Frame image for video generation. Connect a Load Image node to provide the frame image."
}),
"References": ("RUNWAREVIDEOINPUTSREFERENCES", {
"tooltip": "Connect the Video Inputs References node to provide reference images."
}),
@@ -31,6 +34,7 @@ class videoInferenceInputs:
image = kwargs.get("Image", None)
audio = kwargs.get("Audio", None)
mask = kwargs.get("Mask", None)
frame = kwargs.get("Frame", None)
references = kwargs.get("References", None)
inputs = {}
@@ -46,6 +50,10 @@ class videoInferenceInputs:
mask_uuid = rwUtils.convertTensor2IMG(mask)
inputs["mask"] = mask_uuid
if frame is not None:
frame_uuid = rwUtils.convertTensor2IMG(frame)
inputs["frame"] = frame_uuid
if references is not None and len(references) > 0:
inputs["references"] = references
modules/videoModelSearch.py
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@@ -58,6 +58,9 @@ class videoModelSearch:
"lightricks:2@0 (LTX Fast)",
"lightricks:2@1 (LTX Pro)",
],
"Ovi": [
"runware:190@1 (Ovi)",
],
}
# Model dimensions mapping
@@ -118,6 +121,9 @@ class videoModelSearch:
# Lightricks Models
"lightricks:2@0": {"width": 1920, "height": 1080}, # Lightricks v2.0 Fast
"lightricks:2@1": {"width": 1920, "height": 1080}, # Lightricks v2.1 Pro
# Ovi Models
"runware:190@1": {"width": 0, "height": 0}, # Ovi
}
@@ -143,7 +149,8 @@ class videoModelSearch:
"Vidu",
"Wan",
"OpenAI",
"Lightricks"
"Lightricks",
"Ovi"
], {
"tooltip": "Choose Video Model Architecture To Filter Results.",
"default": "KlingAI",
workflows/Runware_Basic_Background_Removal.json
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